Creating Language-driven Spatial Variations of Icon Images
CoRR(2024)
Abstract
Editing 2D icon images can require significant manual effort from designers.
It involves manipulating multiple geometries while maintaining the logical or
physical coherence of the objects depicted in the image. Previous language
driven image editing methods can change the texture and geometry of objects in
the image but fail at producing spatial variations, i.e. modifying spatial
relations between objects while maintaining their identities. We present a
language driven editing method that can produce spatial variations of icon
images. Our method takes in an icon image along with a user's editing request
text prompt and outputs an edited icon image reflecting the user's editing
request. Our method is designed based on two key observations: (1) A user's
editing requests can be translated by a large language model (LLM), with help
from a domain specific language (DSL) library, into to a set of geometrical
constraints defining the relationships between segments in an icon image. (2)
Optimizing the affine transformations of the segments with respect to these
geometrical constraints can produce icon images that fulfill the editing
request and preserve overall physical and logical coherence. Quantitative and
qualitative results show that our system outperforms multiple baselines,
enabling natural editing of icon images.
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